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Commit 37ca9669 authored by Stefano Serafin's avatar Stefano Serafin
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Big rearrangment of the code; First, experiments are run; then, optionally, figures are plotted.

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with 285 additions and 255 deletions
PE_CBL.py
+ 285
− 255
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......@@ -98,10 +98,10 @@ if __name__ == '__main__':
# Decide what figures to plot
fig01 = False
fig02 = True
fig03 = False
fig04 = False
fig05 = False
fig06 = False
fig03 = True
fig04 = True
fig05 = True
fig06 = True
fig07 = True
fig08 = True
......@@ -110,9 +110,11 @@ if __name__ == '__main__':
opt02 = False # assimilation of profiles at two times
# Whether or not to run experiments without parameter estimation
# Applies only to sets B&C; no-PE run is always computed for experiments A&D
noPE_runs = False
# Default PE experiment
############################################################################
# Experiment A (control)
# Create a copy of the default settings
cbl_settings_A = dict(default_cbl_settings)
da_settings_A = dict(default_da_settings)
......@@ -148,131 +150,8 @@ if __name__ == '__main__':
exp_A_noPE = experiment(da_settings_A_noPE)
pickle.dump(exp_A_noPE, open('exp_A_noPE.pickle', 'wb'))
if fig01:
# Create a copy of the default settings
cbl_settings = dict(default_cbl_settings)
# Disable parameter estimation (not used here)
cbl_settings['do_parameter_estimation'] = False
# Panel a) deterministic run using default settings
cbl_det = CBL(cbl_settings)
cbl_det.initialize(1)
cbl_det.run(output_full_history=True)
# Panel b) sensitivity to p; use a smaller domain and higher
# resolution for better-looking plots.
zmax = 2000
p_factors = [0.5,1.5,4.5]
cbl_settings['dz'] = 25
cbl_settings['ztop'] = zmax
theta_profiles = []
for pfac in p_factors:
cbl_settings['pfac'] = pfac
cbl_pf = CBL(cbl_settings)
cbl_pf.initialize(1)
cbl_pf.run()
theta_profiles.append(cbl_pf.x[:cbl_pf.nz])
# Panel c) spread induced by p
# Do a free ensemble run (ensemble size set expliclity)
cbl_settings_free = dict(default_cbl_settings)
cbl_settings_free['perturb_ensemble_state'] = False
cbl_free = CBL(cbl_settings_free)
cbl_free.initialize(nens)
cbl_free.run(output_full_history=True)
# Make plots
ncont = 13
fig, [[ax4, ax2],[ax1, ax3]] = p.subplots(2,2,constrained_layout=True)
fig.set_size_inches(6,6)
c1 = ax1.pcolormesh(cbl_det.history['time']/3600,cbl_det.zt,cbl_det.history['theta'],vmin=290,vmax=296)
ax1.set_ylim([0,zmax])
ax1.set_ylabel(r'Height (m)')
ax1.set_xlabel(r'Time (h)')
ax1.set_xticks(np.arange(4))
ax1.set_title(r'c) $\overline{\theta}$ (K)')
p.colorbar(c1,orientation='horizontal')
ax1.contour(cbl_det.history['time']/3600,cbl_det.zt,cbl_det.history['theta'],
np.linspace(cbl_det.theta_0,cbl_det.theta_0+cbl_det.gamma*zmax,ncont),
colors='white',
linestyles='--',
linewidths=0.75)
ax2 = plot_p(p_factors,theta_profiles,cbl_pf.zt,None,ax=ax2)
ax2.set_ylabel(r'Height (m)')
ax2.set_xlabel(r'$\overline{\theta}$ (K)')
ax2.set_xlim([291,297])
ax2.set_ylim([0,zmax])
ax2.legend(loc=4,frameon=False)
ax2.set_title(r'b) $\overline{\theta}$ sensitivity to $p$')
ax3,c3 = plot_spread(cbl_free,ax=ax3)
ax3.set_ylabel(r'Height (m)')
ax3.set_title(r'd) $\sigma_\theta$ (K)')
ax3.set_xlabel('Time (h)')
ax3.set_xticks(np.arange(4))
p.colorbar(c3,orientation='horizontal')
zoverh = np.linspace(0,1,101)
for pfac in p_factors:
Koverkws = zoverh*(1-zoverh)**pfac
ax4.plot(Koverkws,zoverh,label='$p=%4.1f$'%pfac)
ax4.set_title(r'a) $K_h$ sensitivity to $p$')
ax4.set_xlabel('$K_h/(\kappa w_s h)$')
ax4.set_ylabel('$z/h$')
ax4.set_xlim([0,0.5])
ax4.legend(loc=4,frameon=False)
#p.setp(ax2.get_yticklabels(), visible=False)
#p.setp(ax3.get_yticklabels(), visible=False)
fig.savefig('fig01.png',format='png',dpi=300)
p.close(fig)
if fig02:
# Make plots
fig, [[ax0, ax1], [ax2, ax3]] = p.subplots(2,2,constrained_layout=True)
fig.set_size_inches(6,6)
#
[ax0,ax1,ax2],c0,c1,c2 = plot_CBL_identifiability(exp_A,da_settings_A['obs_error_sdev_assimilate'][0],None,ax=[ax0,ax1,ax2])
ax0.set_title(r'a) Exp. A, $\rho(p\prime\prime,y_b}$)')
ax0.set_xlabel('Time (h)')
ax0.set_ylabel('Height (m)')
ax1.set_title(r'b) Exp. A, $\delta y\cdot(\sigma_{p\prime\prime}/\sigma_{y^b})$')
ax1.set_xlabel('Time (h)')
ax1.set_ylabel('Height (m)')
ax2.set_title(r'c) Exp. A, $\delta p\prime\prime$')
ax2.set_xlabel('Time (h)')
ax2.set_ylabel('Height (m)')
ax3 = plot_CBL_PE(exp_A,None,ax=ax3)
ax3.set_title(r'd) Exp. A, evolution of $p$')
ax3.set_xlabel('Time (h)')
ax3.set_yticks([0,1,2,3,4,5])
p.colorbar(c0,orientation='horizontal')
p.colorbar(c1,orientation='horizontal')
p.colorbar(c2,orientation='horizontal')
#
fig.savefig('fig02.png',format='png',dpi=300)
p.close(fig)
if fig03:
exp_A = pickle.load(open("exp_A.pickle", "rb"))
exp_A_noPE = pickle.load(open("exp_A_noPE.pickle", "rb"))
experiments_pe = [exp_A]
experiments_nope = [exp_A_noPE]
labels = ["Exp. A"]
plot_diagnostics(experiments_pe,experiments_nope,labels,'fig03.png')
if fig04:
########################################################################
# Experiment B1
# Create a copy of the default settings
# Then re-use the available nature run and derived information
cbl_settings_B1 = dict(default_cbl_settings)
......@@ -288,6 +167,9 @@ if __name__ == '__main__':
da_settings_B1['obs_error_sdev_assimilate'] = np.ones(nobs)*sigma_o_as
# Run and save to disk
try:
exp_B1 = pickle.load(open("exp_B1.pickle", "rb"))
except:
exp_B1 = experiment(da_settings_B1)
setattr(exp_B1,'label','B1')
pickle.dump(exp_B1, open('exp_B1.pickle', 'wb'))
......@@ -302,10 +184,14 @@ if __name__ == '__main__':
da_settings_B1_noPE['cbl_settings'] = cbl_settings_B1_noPE
# Run and save to disk
try:
exp_B1_noPE = pickle.load(open("exp_B1_noPE.pickle", "rb"))
except:
exp_B1_noPE = experiment(da_settings_B1_noPE)
pickle.dump(exp_B1_noPE, open('exp_B1_noPE.pickle', 'wb'))
########################################################################
# Experiment B2
# Create a copy of the default settings
# Then re-use the available nature run and derived information
cbl_settings_B2 = dict(default_cbl_settings)
......@@ -321,6 +207,9 @@ if __name__ == '__main__':
da_settings_B2['obs_error_sdev_assimilate'] = np.ones(nobs)*sigma_o_as*10
# Run and save to disk
try:
exp_B2 = pickle.load(open("exp_B2.pickle", "rb"))
except:
exp_B2 = experiment(da_settings_B2)
setattr(exp_B2,'label','B2')
pickle.dump(exp_B2, open('exp_B2.pickle', 'wb'))
......@@ -335,10 +224,14 @@ if __name__ == '__main__':
da_settings_B2_noPE['cbl_settings'] = cbl_settings_B2_noPE
# Run and save to disk
try:
exp_B2_noPE = pickle.load(open("exp_B2_noPE.pickle", "rb"))
except:
exp_B2_noPE = experiment(da_settings_B2_noPE)
pickle.dump(exp_B2_noPE, open('exp_B2_noPE.pickle', 'wb'))
########################################################################
# Experiment B3
# Create a copy of the default settings
# Then re-use the available nature run and derived information
cbl_settings_B3 = dict(default_cbl_settings)
......@@ -354,6 +247,9 @@ if __name__ == '__main__':
da_settings_B3['obs_error_sdev_assimilate'] = np.ones(nobs)*sigma_o_as*10
# Run and save to disk
try:
exp_B3 = pickle.load(open("exp_B3.pickle", "rb"))
except:
exp_B3 = experiment(da_settings_B3)
setattr(exp_B3,'label','B3')
pickle.dump(exp_B3, open('exp_B3.pickle', 'wb'))
......@@ -368,10 +264,14 @@ if __name__ == '__main__':
da_settings_B3_noPE['cbl_settings'] = cbl_settings_B3_noPE
# Run and save to disk
try:
exp_B3_noPE = pickle.load(open("exp_B3_noPE.pickle", "rb"))
except:
exp_B3_noPE = experiment(da_settings_B3_noPE)
pickle.dump(exp_B3_noPE, open('exp_B3_noPE.pickle', 'wb'))
########################################################################
# Experiment B4
# Create a copy of the default settings
cbl_settings_B4 = dict(default_cbl_settings)
da_settings_B4 = dict(default_da_settings)
......@@ -385,6 +285,9 @@ if __name__ == '__main__':
da_settings_B4['obs_error_sdev_assimilate'] = np.ones(nobs)*sigma_o_as*10
# Run and save to disk
try:
exp_B4 = pickle.load(open("exp_B4.pickle", "rb"))
except:
exp_B4 = experiment(da_settings_B4)
setattr(exp_B4,'label','B4')
pickle.dump(exp_B4, open('exp_B4.pickle', 'wb'))
......@@ -397,46 +300,14 @@ if __name__ == '__main__':
da_settings_B4_noPE['cbl_settings'] = cbl_settings_B4_noPE
# Run and save to disk
try:
exp_B4_noPE = pickle.load(open("exp_B4_noPE.pickle", "rb"))
except:
exp_B4_noPE = experiment(da_settings_B4_noPE)
pickle.dump(exp_B4_noPE, open('exp_B4_noPE.pickle', 'wb'))
########################################################################
# Make plots
fig, [[ax1, ax2], [ax3, ax4]] = p.subplots(2,2,constrained_layout=True)
fig.set_size_inches(6,4)
#
ax1 = plot_CBL_PE(exp_B1,None,ax=ax1)
ax1.set_title(r'a) Exp. B$_1$')
ax1.set_xlabel('Time (h)')
ax1.set_ylabel(r'$p$')
#
ax2 = plot_CBL_PE(exp_B2,None,ax=ax2)
ax2.set_title(r'b) Exp. B$_2$')
ax2.set_xlabel('Time (h)')
ax2.set_ylabel(r'$p$')
#
ax3 = plot_CBL_PE(exp_B3,None,ax=ax3)
ax3.set_title(r'c) Exp. B$_3$')
ax3.set_xlabel('Time (h)')
ax3.set_ylabel(r'$p$')
#
ax4 = plot_CBL_PE(exp_B4,None,ax=ax4)
ax4.set_title(r'd) Exp. B$_4$')
ax4.set_xlabel('Time (h)')
ax4.set_ylabel(r'$p$')
#
ax1.set_yticks([0,1,2,3,4,5])
ax2.sharey(ax1)
ax3.set_yticks([0,1,2,3,4,5])
ax4.sharey(ax3)
#
fig.savefig('fig04.png',format='png',dpi=300)
p.close(fig)
if fig05:
########################################################################
# Experiment C1
# Create a copy of the default settings
cbl_settings_C1 = dict(default_cbl_settings)
da_settings_C1 = dict(default_da_settings)
......@@ -452,6 +323,9 @@ if __name__ == '__main__':
da_settings_C1['cbl_settings'] = cbl_settings_C1
# Run and save to disk
try:
exp_C1 = pickle.load(open("exp_C1.pickle", "rb"))
except:
exp_C1 = experiment(da_settings_C1)
setattr(exp_C1,'label','C1')
pickle.dump(exp_C1, open('exp_C1.pickle', 'wb'))
......@@ -464,10 +338,14 @@ if __name__ == '__main__':
da_settings_C1_noPE['cbl_settings'] = cbl_settings_C1_noPE
# Run and save to disk
try:
exp_C1_noPE = pickle.load(open("exp_C1_noPE.pickle", "rb"))
except:
exp_C1_noPE = experiment(da_settings_C1_noPE)
pickle.dump(exp_C1_noPE, open('exp_C1_noPE.pickle', 'wb'))
########################################################################
# Experiment C2
# Create a copy of the default settings
cbl_settings_C2 = dict(default_cbl_settings)
da_settings_C2 = dict(default_da_settings)
......@@ -483,6 +361,9 @@ if __name__ == '__main__':
da_settings_C2['cbl_settings'] = cbl_settings_C2
# Run and save to disk
try:
exp_C2 = pickle.load(open("exp_C2.pickle", "rb"))
except:
exp_C2 = experiment(da_settings_C2)
setattr(exp_C2,'label','C2')
pickle.dump(exp_C2, open('exp_C2.pickle', 'wb'))
......@@ -495,11 +376,209 @@ if __name__ == '__main__':
da_settings_C2_noPE['cbl_settings'] = cbl_settings_C2_noPE
# Run and save to disk
try:
exp_C2_noPE = pickle.load(open("exp_C2_noPE.pickle", "rb"))
except:
exp_C2_noPE = experiment(da_settings_C2_noPE)
pickle.dump(exp_C2_noPE, open('exp_C2_noPE.pickle', 'wb'))
########################################################################
# Experiment D
# Create a copy of the default settings
cbl_settings_D = dict(default_cbl_settings)
da_settings_D = dict(default_da_settings)
# Change settings as necessary
# Changes include generation of observations, so the existing nature run
# can't be reused.
cbl_settings_D['initial_perturbed_parameters'] = exp_A.da.initial_perturbed_parameters
cbl_settings_D['perturbations_theta_amplitude'] = sigma_b_init*10
cbl_settings_D['Hmax'] = 0.15
cbl_settings_D['is_cgrad'] = False
cbl_settings_D['simulate_error_growth'] = True
cbl_settings_D['error_growth_perturbations_amplitude'] = sigma_b_init*5
da_settings_D['cbl_settings'] = cbl_settings_D
da_settings_D['obs_error_sdev_generate'] = np.ones(nobs)*sigma_o_as*5
da_settings_D['obs_error_sdev_assimilate'] = np.ones(nobs)*sigma_o_as*10
# Run and save to disk
try:
exp_D = pickle.load(open("exp_D.pickle", "rb"))
except:
exp_D = experiment(da_settings_D)
setattr(exp_D,'label','D')
pickle.dump(exp_D, open('exp_D.pickle', 'wb'))
# Experiment matching D, but without parameter estimation
cbl_settings_D_noPE = dict(cbl_settings_D)
da_settings_D_noPE = dict(da_settings_D)
cbl_settings_D_noPE['do_parameter_estimation'] = False
da_settings_D_noPE['cbl_settings'] = cbl_settings_D_noPE
try:
exp_D_noPE = pickle.load(open("exp_D_noPE.pickle", "rb"))
except:
exp_D_noPE = experiment(da_settings_D_noPE)
pickle.dump(exp_A_noPE, open('exp_D_noPE.pickle', 'wb'))
########################################################################
if fig01:
# Create a copy of the default settings
cbl_settings = dict(default_cbl_settings)
# Disable parameter estimation (not used here)
cbl_settings['do_parameter_estimation'] = False
# Panel a) deterministic run using default settings
cbl_det = CBL(cbl_settings)
cbl_det.initialize(1)
cbl_det.run(output_full_history=True)
# Panel b) sensitivity to p; use a smaller domain and higher
# resolution for better-looking plots.
zmax = 2000
p_factors = [0.5,1.5,4.5]
cbl_settings['dz'] = 25
cbl_settings['ztop'] = zmax
theta_profiles = []
for pfac in p_factors:
cbl_settings['pfac'] = pfac
cbl_pf = CBL(cbl_settings)
cbl_pf.initialize(1)
cbl_pf.run()
theta_profiles.append(cbl_pf.x[:cbl_pf.nz])
# Panel c) spread induced by p
# Do a free ensemble run (ensemble size set expliclity)
cbl_settings_free = dict(default_cbl_settings)
cbl_settings_free['perturb_ensemble_state'] = False
cbl_free = CBL(cbl_settings_free)
cbl_free.initialize(nens)
cbl_free.run(output_full_history=True)
# Make plots
ncont = 13
fig, [[ax4, ax2],[ax1, ax3]] = p.subplots(2,2,constrained_layout=True)
fig.set_size_inches(6,6)
c1 = ax1.pcolormesh(cbl_det.history['time']/3600,cbl_det.zt,cbl_det.history['theta'],vmin=290,vmax=296)
ax1.set_ylim([0,zmax])
ax1.set_ylabel(r'Height (m)')
ax1.set_xlabel(r'Time (h)')
ax1.set_xticks(np.arange(4))
ax1.set_title(r'c) $\overline{\theta}$ (K)')
p.colorbar(c1,orientation='horizontal')
ax1.contour(cbl_det.history['time']/3600,cbl_det.zt,cbl_det.history['theta'],
np.linspace(cbl_det.theta_0,cbl_det.theta_0+cbl_det.gamma*zmax,ncont),
colors='white',
linestyles='--',
linewidths=0.75)
ax2 = plot_p(p_factors,theta_profiles,cbl_pf.zt,None,ax=ax2)
ax2.set_ylabel(r'Height (m)')
ax2.set_xlabel(r'$\overline{\theta}$ (K)')
ax2.set_xlim([291,297])
ax2.set_ylim([0,zmax])
ax2.legend(loc=4,frameon=False)
ax2.set_title(r'b) $\overline{\theta}$ sensitivity to $p$')
ax3,c3 = plot_spread(cbl_free,ax=ax3)
ax3.set_ylabel(r'Height (m)')
ax3.set_title(r'd) $\sigma_\theta$ (K)')
ax3.set_xlabel('Time (h)')
ax3.set_xticks(np.arange(4))
p.colorbar(c3,orientation='horizontal')
zoverh = np.linspace(0,1,101)
for pfac in p_factors:
Koverkws = zoverh*(1-zoverh)**pfac
ax4.plot(Koverkws,zoverh,label='$p=%4.1f$'%pfac)
ax4.set_title(r'a) $K_h$ sensitivity to $p$')
ax4.set_xlabel('$K_h/(\kappa w_s h)$')
ax4.set_ylabel('$z/h$')
ax4.set_xlim([0,0.5])
ax4.legend(loc=4,frameon=False)
#p.setp(ax2.get_yticklabels(), visible=False)
#p.setp(ax3.get_yticklabels(), visible=False)
fig.savefig('fig01.png',format='png',dpi=300)
p.close(fig)
if fig02:
fig, [[ax0, ax1], [ax2, ax3]] = p.subplots(2,2,constrained_layout=True)
fig.set_size_inches(6,6)
#
[ax0,ax1,ax2],c0,c1,c2 = plot_CBL_identifiability(exp_A,da_settings_A['obs_error_sdev_assimilate'][0],None,ax=[ax0,ax1,ax2])
ax0.set_title(r'a) Exp. A, $\rho(p\prime\prime,y_b}$)')
ax0.set_xlabel('Time (h)')
ax0.set_ylabel('Height (m)')
ax1.set_title(r'b) Exp. A, $\delta y\cdot(\sigma_{p\prime\prime}/\sigma_{y^b})$')
ax1.set_xlabel('Time (h)')
ax1.set_ylabel('Height (m)')
ax2.set_title(r'c) Exp. A, $\delta p\prime\prime$')
ax2.set_xlabel('Time (h)')
ax2.set_ylabel('Height (m)')
ax3 = plot_CBL_PE(exp_A,None,ax=ax3)
ax3.set_title(r'd) Exp. A, evolution of $p$')
ax3.set_xlabel('Time (h)')
ax3.set_yticks([0,1,2,3,4,5])
p.colorbar(c0,orientation='horizontal')
p.colorbar(c1,orientation='horizontal')
p.colorbar(c2,orientation='horizontal')
#
fig.savefig('fig02.png',format='png',dpi=300)
p.close(fig)
if fig03:
exp_A = pickle.load(open("exp_A.pickle", "rb"))
exp_A_noPE = pickle.load(open("exp_A_noPE.pickle", "rb"))
experiments_pe = [exp_A]
experiments_nope = [exp_A_noPE]
labels = ["Exp. A"]
plot_diagnostics(experiments_pe,experiments_nope,labels,'fig03.png')
if fig04:
fig, [[ax1, ax2], [ax3, ax4]] = p.subplots(2,2,constrained_layout=True)
fig.set_size_inches(6,4)
#
ax1 = plot_CBL_PE(exp_B1,None,ax=ax1)
ax1.set_title(r'a) Exp. B$_1$')
ax1.set_xlabel('Time (h)')
ax1.set_ylabel(r'$p$')
#
ax2 = plot_CBL_PE(exp_B2,None,ax=ax2)
ax2.set_title(r'b) Exp. B$_2$')
ax2.set_xlabel('Time (h)')
ax2.set_ylabel(r'$p$')
#
ax3 = plot_CBL_PE(exp_B3,None,ax=ax3)
ax3.set_title(r'c) Exp. B$_3$')
ax3.set_xlabel('Time (h)')
ax3.set_ylabel(r'$p$')
#
ax4 = plot_CBL_PE(exp_B4,None,ax=ax4)
ax4.set_title(r'd) Exp. B$_4$')
ax4.set_xlabel('Time (h)')
ax4.set_ylabel(r'$p$')
#
ax1.set_yticks([0,1,2,3,4,5])
ax2.sharey(ax1)
ax3.set_yticks([0,1,2,3,4,5])
ax4.sharey(ax3)
#
fig.savefig('fig04.png',format='png',dpi=300)
p.close(fig)
if fig05:
fig, [ax1, ax2] = p.subplots(1,2,constrained_layout=True)
fig.set_size_inches(6,2)
#
......@@ -550,29 +629,6 @@ if __name__ == '__main__':
if fig07:
# Create a copy of the default settings
cbl_settings_D = dict(default_cbl_settings)
da_settings_D = dict(default_da_settings)
# Change settings as necessary
# Changes include generation of observations, so the existing nature run
# can't be reused.
cbl_settings_D['initial_perturbed_parameters'] = exp_A.da.initial_perturbed_parameters
cbl_settings_D['perturbations_theta_amplitude'] = sigma_b_init*10
cbl_settings_D['Hmax'] = 0.15
cbl_settings_D['is_cgrad'] = False
cbl_settings_D['simulate_error_growth'] = True
cbl_settings_D['error_growth_perturbations_amplitude'] = sigma_b_init*10
da_settings_D['cbl_settings'] = cbl_settings_D
da_settings_D['obs_error_sdev_generate'] = np.ones(nobs)*sigma_o_as*5
da_settings_D['obs_error_sdev_assimilate'] = np.ones(nobs)*sigma_o_as*10
# Run and save to disk
exp_D = experiment(da_settings_D)
setattr(exp_D,'label','D')
pickle.dump(exp_D, open('exp_D.pickle', 'wb'))
# Make plots
fig, [[ax0, ax1],[ax2,ax3]] = p.subplots(2,2,constrained_layout=True)
fig.set_size_inches(6,6)
#
......@@ -600,41 +656,15 @@ if __name__ == '__main__':
if fig08:
# Create a copy of the default settings
cbl_settings_D = dict(default_cbl_settings)
da_settings_D = dict(default_da_settings)
# Change settings as necessary
# Changes include generation of observations, so the existing nature run
# can't be reused.
cbl_settings_D['initial_perturbed_parameters'] = exp_A.da.initial_perturbed_parameters
cbl_settings_D['perturbations_theta_amplitude'] = sigma_b_init*10
cbl_settings_D['Hmax'] = 0.15
cbl_settings_D['is_cgrad'] = False
cbl_settings_D['simulate_error_growth'] = True
cbl_settings_D['error_growth_perturbations_amplitude'] = sigma_b_init*10
da_settings_D['cbl_settings'] = cbl_settings_D
da_settings_D['obs_error_sdev_generate'] = np.ones(nobs)*sigma_o_as*10
da_settings_D['obs_error_sdev_assimilate'] = np.ones(nobs)*sigma_o_as*10
# Experiment matching D, but without parameter estimation
cbl_settings_D_noPE = dict(cbl_settings_D)
da_settings_D_noPE = dict(da_settings_D)
cbl_settings_D_noPE['do_parameter_estimation'] = False
da_settings_D_noPE['cbl_settings'] = cbl_settings_D_noPE
exp_D = pickle.load(open("exp_D.pickle", "rb"))
try:
exp_D_noPE = pickle.load(open("exp_D_noPE.pickle", "rb"))
except:
exp_D_noPE = experiment(da_settings_D_noPE)
pickle.dump(exp_A_noPE, open('exp_D_noPE.pickle', 'wb'))
experiments_pe = [exp_A]
experiments_nope = [exp_A_noPE]
labels = ["Exp. A"]
plot_diagnostics(experiments_pe,experiments_nope,labels,'fig08a.png')
experiments_pe = [exp_D]
experiments_nope = [exp_D_noPE]
labels = ["Exp. D"]
plot_diagnostics(experiments_pe,experiments_nope,labels,'fig08.png')
plot_diagnostics(experiments_pe,experiments_nope,labels,'fig08d.png')
if opt01:
......
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